Elements of information theory
Elements of information theory
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Automatic programming of morphological machines by PAC learning
Fundamenta Informaticae - Special issue on mathematical morphology
Multiresolution Analysis for Optimal Binary Filters
Journal of Mathematical Imaging and Vision
Feature Selection Based on Fuzzy Distances between Clusters: First Results on Simulated Data
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
W-Operator Window Design by Maximization of Training Data Information
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Feature selection and feature extraction for text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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This paper generalizes the technique described in [1] to gray-scale image processing applications. This method chooses a subset of variables W (i.e. pixels seen through a window) that maximizes the information observed in a set of training data by mean conditional entropy minimization. The task is formalized as a combinatorial optimization problem, where the search space is the powerset of the candidate variables and the measure to be minimized is the mean entropy of the estimated conditional probabilities. As a full exploration of the search space requires an enormous computational effort, some heuristics of the feature selection literature are applied. The introduced approach is mathematically sound and experimental results with texture recognition application show that it is also adequate to treat problems with gray-scale images.